Overview

Dataset statistics

Number of variables14
Number of observations5693
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory622.8 KiB
Average record size in memory112.0 B

Variable types

Numeric14

Alerts

revenue is highly correlated with quantity_orders and 3 other fieldsHigh correlation
recency is highly correlated with quantity_orders and 2 other fieldsHigh correlation
quantity_orders is highly correlated with revenue and 7 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 4 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 3 other fieldsHigh correlation
avg_recency is highly correlated with recency and 2 other fieldsHigh correlation
frequency is highly correlated with recency and 2 other fieldsHigh correlation
frequency_btwn_purchases is highly correlated with quantity_orders and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with revenue and 3 other fieldsHigh correlation
avg_unique_basked_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
quantity_items_returned is highly correlated with quantity_orders and 1 other fieldsHigh correlation
monetary_returned is highly correlated with quantity_orders and 1 other fieldsHigh correlation
revenue is highly correlated with quantity_orders and 1 other fieldsHigh correlation
recency is highly correlated with avg_recencyHigh correlation
quantity_orders is highly correlated with revenue and 1 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 1 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_sizeHigh correlation
avg_recency is highly correlated with recencyHigh correlation
avg_basket_size is highly correlated with avg_ticketHigh correlation
quantity_items_returned is highly correlated with monetary_returnedHigh correlation
monetary_returned is highly correlated with quantity_items_returnedHigh correlation
revenue is highly correlated with quantity_orders and 3 other fieldsHigh correlation
recency is highly correlated with avg_recency and 1 other fieldsHigh correlation
quantity_orders is highly correlated with revenue and 2 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 1 other fieldsHigh correlation
avg_recency is highly correlated with recency and 1 other fieldsHigh correlation
frequency is highly correlated with recency and 1 other fieldsHigh correlation
frequency_btwn_purchases is highly correlated with quantity_ordersHigh correlation
avg_basket_size is highly correlated with revenue and 2 other fieldsHigh correlation
quantity_items_returned is highly correlated with monetary_returnedHigh correlation
monetary_returned is highly correlated with quantity_items_returnedHigh correlation
customer_id is highly correlated with recency and 2 other fieldsHigh correlation
revenue is highly correlated with quantity_orders and 4 other fieldsHigh correlation
recency is highly correlated with customer_id and 2 other fieldsHigh correlation
quantity_orders is highly correlated with revenue and 3 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 4 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_size and 1 other fieldsHigh correlation
avg_recency is highly correlated with customer_id and 2 other fieldsHigh correlation
time_in_base is highly correlated with customer_id and 2 other fieldsHigh correlation
frequency is highly correlated with revenue and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 2 other fieldsHigh correlation
avg_unique_basked_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
quantity_items_returned is highly correlated with revenue and 5 other fieldsHigh correlation
monetary_returned is highly correlated with revenue and 2 other fieldsHigh correlation
revenue is highly skewed (γ1 = 23.01115224) Skewed
quantity_items_purchased is highly skewed (γ1 = 25.09928996) Skewed
avg_ticket is highly skewed (γ1 = 20.84844077) Skewed
quantity_items_returned is highly skewed (γ1 = 29.45080834) Skewed
monetary_returned is highly skewed (γ1 = 35.43664486) Skewed
customer_id has unique values Unique
quantity_items_returned has 4190 (73.6%) zeros Zeros
monetary_returned has 4190 (73.6%) zeros Zeros

Reproduction

Analysis started2022-05-04 19:22:21.346532
Analysis finished2022-05-04 19:23:27.797284
Duration1 minute and 6.45 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5693
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16601.48287
Minimum12347
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:28.137089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12700.6
Q114289
median16229
Q318211
95-th percentile21732.8
Maximum22709
Range10362
Interquartile range (IQR)3922

Descriptive statistics

Standard deviation2808.14998
Coefficient of variation (CV)0.1691505513
Kurtosis-0.8215480997
Mean16601.48287
Median Absolute Deviation (MAD)1962
Skewness0.4411393053
Sum94512242
Variance7885706.31
MonotonicityNot monotonic
2022-05-04T16:23:28.524756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
161231
 
< 0.1%
153351
 
< 0.1%
175341
 
< 0.1%
172051
 
< 0.1%
164121
 
< 0.1%
139231
 
< 0.1%
175201
 
< 0.1%
172011
 
< 0.1%
165631
 
< 0.1%
Other values (5683)5683
99.8%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
123571
< 0.1%
ValueCountFrequency (%)
227091
< 0.1%
227081
< 0.1%
227071
< 0.1%
227061
< 0.1%
227051
< 0.1%
227041
< 0.1%
227001
< 0.1%
226991
< 0.1%
226961
< 0.1%
226951
< 0.1%

revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5447
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1761.340198
Minimum0.42
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:28.910585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile13.128
Q1236.18
median613.2
Q31570.45
95-th percentile5302.148
Maximum279138.02
Range279137.6
Interquartile range (IQR)1334.27

Descriptive statistics

Standard deviation7517.330182
Coefficient of variation (CV)4.26796038
Kurtosis697.9733466
Mean1761.340198
Median Absolute Deviation (MAD)479.16
Skewness23.01115224
Sum10027309.75
Variance56510253.07
MonotonicityNot monotonic
2022-05-04T16:23:29.206011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.959
 
0.2%
1.258
 
0.1%
4.958
 
0.1%
2.958
 
0.1%
12.757
 
0.1%
1.657
 
0.1%
3.757
 
0.1%
5.956
 
0.1%
7.56
 
0.1%
4.256
 
0.1%
Other values (5437)5621
98.7%
ValueCountFrequency (%)
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
0.1%
1.441
 
< 0.1%
1.657
0.1%
1.691
 
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.8909187
Minimum0
Maximum373
Zeros37
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:29.574092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median71
Q3200
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)177

Descriptive statistics

Standard deviation111.6046783
Coefficient of variation (CV)0.9547762958
Kurtosis-0.6424840601
Mean116.8909187
Median Absolute Deviation (MAD)61
Skewness0.8143393856
Sum665460
Variance12455.60423
MonotonicityNot monotonic
2022-05-04T16:23:29.858948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1110
 
1.9%
4105
 
1.8%
398
 
1.7%
291
 
1.6%
1086
 
1.5%
882
 
1.4%
1779
 
1.4%
979
 
1.4%
778
 
1.4%
1567
 
1.2%
Other values (294)4818
84.6%
ValueCountFrequency (%)
037
 
0.6%
1110
1.9%
291
1.6%
398
1.7%
4105
1.8%
552
0.9%
778
1.4%
882
1.4%
979
1.4%
1086
1.5%
ValueCountFrequency (%)
37323
0.4%
37222
0.4%
37117
0.3%
3694
 
0.1%
36813
0.2%
36716
0.3%
36615
0.3%
36519
0.3%
36411
0.2%
3627
 
0.1%

quantity_orders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.470402248
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:30.166760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range205
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.81053531
Coefficient of variation (CV)1.962462799
Kurtosis302.473391
Mean3.470402248
Median Absolute Deviation (MAD)0
Skewness13.19909941
Sum19757
Variance46.38339121
MonotonicityNot monotonic
2022-05-04T16:23:30.540535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12870
50.4%
2826
 
14.5%
3501
 
8.8%
4395
 
6.9%
5236
 
4.1%
6173
 
3.0%
7139
 
2.4%
898
 
1.7%
968
 
1.2%
1055
 
1.0%
Other values (46)332
 
5.8%
ValueCountFrequency (%)
12870
50.4%
2826
 
14.5%
3501
 
8.8%
4395
 
6.9%
5236
 
4.1%
6173
 
3.0%
7139
 
2.4%
898
 
1.7%
968
 
1.2%
1055
 
1.0%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
911
< 0.1%
901
< 0.1%
861
< 0.1%
721
< 0.1%
622
< 0.1%
601
< 0.1%

quantity_items_purchased
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1840
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean951.7265062
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:30.819092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q1106
median317
Q3804
95-th percentile2925.2
Maximum196844
Range196843
Interquartile range (IQR)698

Descriptive statistics

Standard deviation4189.903881
Coefficient of variation (CV)4.402424282
Kurtosis942.5150692
Mean951.7265062
Median Absolute Deviation (MAD)253
Skewness25.09928996
Sum5418179
Variance17555294.53
MonotonicityNot monotonic
2022-05-04T16:23:31.140867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1113
 
2.0%
273
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1225
 
0.4%
8822
 
0.4%
7221
 
0.4%
720
 
0.4%
Other values (1830)5255
92.3%
ValueCountFrequency (%)
1113
2.0%
273
1.3%
351
0.9%
449
0.9%
535
 
0.6%
629
 
0.5%
720
 
0.4%
818
 
0.3%
97
 
0.1%
1017
 
0.3%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5452
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean554.0214815
Minimum0.42
Maximum52940.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:31.423966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile12.83
Q1158.95
median297.38
Q3486.6742857
95-th percentile1840.048
Maximum52940.94
Range52940.52
Interquartile range (IQR)327.7242857

Descriptive statistics

Standard deviation1380.286726
Coefficient of variation (CV)2.49139568
Kurtosis694.0198078
Mean554.0214815
Median Absolute Deviation (MAD)152.3
Skewness20.84844077
Sum3154044.294
Variance1905191.445
MonotonicityNot monotonic
2022-05-04T16:23:31.732067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.959
 
0.2%
1.258
 
0.1%
2.958
 
0.1%
4.958
 
0.1%
12.757
 
0.1%
3.757
 
0.1%
1.657
 
0.1%
5.956
 
0.1%
7.56
 
0.1%
4.256
 
0.1%
Other values (5442)5621
98.7%
ValueCountFrequency (%)
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
0.1%
1.441
 
< 0.1%
1.657
0.1%
1.691
 
< 0.1%
ValueCountFrequency (%)
52940.941
< 0.1%
50653.911
< 0.1%
21389.61
< 0.1%
18745.861
< 0.1%
14855.531
< 0.1%
14844.766671
< 0.1%
13305.51
< 0.1%
12681.581
< 0.1%
12633.671
< 0.1%
12172.091
< 0.1%

avg_recency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1181
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.9936202
Minimum0
Maximum373
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:32.005116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q144.125
median86
Q3184
95-th percentile336.4
Maximum373
Range373
Interquartile range (IQR)139.875

Descriptive statistics

Standard deviation101.7956195
Coefficient of variation (CV)0.8209746548
Kurtosis-0.2540875244
Mean123.9936202
Median Absolute Deviation (MAD)55.33333333
Skewness0.9376025916
Sum705895.6796
Variance10362.34815
MonotonicityNot monotonic
2022-05-04T16:23:32.275198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6032
 
0.6%
5331
 
0.5%
21330
 
0.5%
35330
 
0.5%
18429
 
0.5%
4628
 
0.5%
6427
 
0.5%
2827
 
0.5%
7726
 
0.5%
15425
 
0.4%
Other values (1171)5408
95.0%
ValueCountFrequency (%)
04
 
0.1%
111
0.2%
27
 
0.1%
2.8473282441
 
< 0.1%
313
0.2%
3.3008849561
 
< 0.1%
3.3303571431
 
< 0.1%
3.3333333331
 
< 0.1%
418
0.3%
4.1444444441
 
< 0.1%
ValueCountFrequency (%)
37323
0.4%
37221
0.4%
37117
0.3%
3694
 
0.1%
36813
0.2%
36716
0.3%
36614
0.2%
36519
0.3%
36411
0.2%
3627
 
0.1%

time_in_base
Real number (ℝ≥0)

HIGH CORRELATION

Distinct305
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.2320393
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:32.555085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q1110
median239
Q3319
95-th percentile370
Maximum374
Range373
Interquartile range (IQR)209

Descriptive statistics

Standard deviation116.5955573
Coefficient of variation (CV)0.5367327841
Kurtosis-1.23398492
Mean217.2320393
Median Absolute Deviation (MAD)96
Skewness-0.2945052057
Sum1236702
Variance13594.52398
MonotonicityNot monotonic
2022-05-04T16:23:32.878057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
374101
 
1.8%
37397
 
1.7%
36788
 
1.5%
36978
 
1.4%
36676
 
1.3%
37070
 
1.2%
35966
 
1.2%
36861
 
1.1%
37257
 
1.0%
36046
 
0.8%
Other values (295)4953
87.0%
ValueCountFrequency (%)
14
 
0.1%
211
0.2%
37
 
0.1%
413
0.2%
518
0.3%
69
0.2%
813
0.2%
96
 
0.1%
1014
0.2%
1121
0.4%
ValueCountFrequency (%)
374101
1.8%
37397
1.7%
37257
1.0%
37070
1.2%
36978
1.4%
36861
1.1%
36788
1.5%
36676
1.3%
36545
0.8%
36332
 
0.6%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1222
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02307007074
Minimum0.002673796791
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:33.273718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.002673796791
5-th percentile0.00296735905
Q10.005449591281
median0.01201201201
Q30.02388059701
95-th percentile0.0688233203
Maximum1
Range0.9973262032
Interquartile range (IQR)0.01843100573

Descriptive statistics

Standard deviation0.04829054538
Coefficient of variation (CV)2.093211847
Kurtosis173.2740082
Mean0.02307007074
Median Absolute Deviation (MAD)0.00750018311
Skewness10.79591569
Sum131.3379127
Variance0.002331976773
MonotonicityNot monotonic
2022-05-04T16:23:33.725563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0185185185237
 
0.6%
0.00540540540532
 
0.6%
0.0163934426231
 
0.5%
0.00282485875730
 
0.5%
0.00467289719630
 
0.5%
0.0153846153829
 
0.5%
0.0526315789529
 
0.5%
0.0192307692328
 
0.5%
0.02527
 
0.5%
0.0454545454526
 
0.5%
Other values (1212)5394
94.7%
ValueCountFrequency (%)
0.00267379679122
0.4%
0.00268096514721
0.4%
0.00268817204317
0.3%
0.0027027027033
 
0.1%
0.002710027113
0.2%
0.00271739130416
0.3%
0.0027247956414
0.2%
0.00273224043719
0.3%
0.00273972602711
0.2%
0.0027548209377
 
0.1%
ValueCountFrequency (%)
15
0.1%
0.5508021391
 
< 0.1%
0.53208556151
 
< 0.1%
0.511
0.2%
0.41
 
< 0.1%
0.33333333336
0.1%
0.33155080211
 
< 0.1%
0.31578947371
 
< 0.1%
0.27272727272
 
< 0.1%
0.26216216221
 
< 0.1%

frequency_btwn_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1225
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5475856325
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:34.165755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.01104159896
Q10.02492211838
median1
Q31
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.9750778816

Descriptive statistics

Standard deviation0.550614711
Coefficient of variation (CV)1.005531698
Kurtosis138.8163381
Mean0.5475856325
Median Absolute Deviation (MAD)0
Skewness4.852587408
Sum3117.405006
Variance0.3031765599
MonotonicityNot monotonic
2022-05-04T16:23:34.663506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12878
50.6%
248
 
0.8%
0.062518
 
0.3%
0.0277777777817
 
0.3%
0.0238095238116
 
0.3%
0.0909090909115
 
0.3%
0.0833333333315
 
0.3%
0.0344827586214
 
0.2%
0.0294117647114
 
0.2%
0.0192307692313
 
0.2%
Other values (1215)2645
46.5%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
< 0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
41
 
< 0.1%
35
 
0.1%
248
 
0.8%
1.1428571431
 
< 0.1%
12878
50.6%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2369
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.215404
Minimum1
Maximum14149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:35.243567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q175
median152
Q3290.625
95-th percentile732.55
Maximum14149
Range14148
Interquartile range (IQR)215.625

Descriptive statistics

Standard deviation439.4962599
Coefficient of variation (CV)1.770624437
Kurtosis378.6707145
Mean248.215404
Median Absolute Deviation (MAD)96.57142857
Skewness14.57359157
Sum1413090.295
Variance193156.9625
MonotonicityNot monotonic
2022-05-04T16:23:35.558557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114
 
2.0%
272
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1226
 
0.5%
7222
 
0.4%
10022
 
0.4%
7321
 
0.4%
Other values (2359)5252
92.3%
ValueCountFrequency (%)
1114
2.0%
272
1.3%
351
0.9%
3.3333333331
 
< 0.1%
449
0.9%
535
 
0.6%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
629
 
0.5%
6.1428571431
 
< 0.1%
ValueCountFrequency (%)
141491
< 0.1%
139561
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
59631
< 0.1%
51971
< 0.1%
43001
< 0.1%
42821
< 0.1%
42801
< 0.1%
41361
< 0.1%

avg_unique_basked_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1172
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.27433576
Minimum0.2
Maximum1109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:35.889666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1
Q17.25
median15
Q331
95-th percentile173
Maximum1109
Range1108.8
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation76.89257379
Coefficient of variation (CV)2.062882469
Kurtosis32.87848226
Mean37.27433576
Median Absolute Deviation (MAD)10
Skewness5.072750237
Sum212202.7935
Variance5912.467904
MonotonicityNot monotonic
2022-05-04T16:23:36.168511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1276
 
4.8%
2161
 
2.8%
3114
 
2.0%
9105
 
1.8%
10105
 
1.8%
8103
 
1.8%
5102
 
1.8%
7101
 
1.8%
6101
 
1.8%
1397
 
1.7%
Other values (1162)4428
77.8%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333337
0.1%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.2%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
11091
< 0.1%
7481
< 0.1%
7301
< 0.1%
7201
< 0.1%
7031
< 0.1%
6861
< 0.1%
6751
< 0.1%
6731
< 0.1%
6601
< 0.1%
6491
< 0.1%

quantity_items_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct214
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.83769542
Minimum0
Maximum9360
Zeros4190
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:36.462730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile38
Maximum9360
Range9360
Interquartile range (IQR)1

Descriptive statistics

Standard deviation239.433569
Coefficient of variation (CV)12.06962623
Kurtosis1016.856308
Mean19.83769542
Median Absolute Deviation (MAD)0
Skewness29.45080834
Sum112936
Variance57328.43395
MonotonicityNot monotonic
2022-05-04T16:23:36.734111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04190
73.6%
1169
 
3.0%
2150
 
2.6%
3105
 
1.8%
489
 
1.6%
678
 
1.4%
561
 
1.1%
1252
 
0.9%
744
 
0.8%
843
 
0.8%
Other values (204)712
 
12.5%
ValueCountFrequency (%)
04190
73.6%
1169
 
3.0%
2150
 
2.6%
3105
 
1.8%
489
 
1.6%
561
 
1.1%
678
 
1.4%
744
 
0.8%
843
 
0.8%
941
 
0.7%
ValueCountFrequency (%)
93601
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

monetary_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1085
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.49914808
Minimum0
Maximum22998.4
Zeros4190
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-05-04T16:23:37.066145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.75
95-th percentile105.936
Maximum22998.4
Range22998.4
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation438.5905076
Coefficient of variation (CV)11.10379664
Kurtosis1590.03785
Mean39.49914808
Median Absolute Deviation (MAD)0
Skewness35.43664486
Sum224868.65
Variance192361.6334
MonotonicityNot monotonic
2022-05-04T16:23:37.412325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04190
73.6%
12.7520
 
0.4%
4.9519
 
0.3%
9.9517
 
0.3%
1517
 
0.3%
5.912
 
0.2%
25.511
 
0.2%
4.2510
 
0.2%
3.759
 
0.2%
19.98
 
0.1%
Other values (1075)1380
 
24.2%
ValueCountFrequency (%)
04190
73.6%
0.422
 
< 0.1%
0.651
 
< 0.1%
0.951
 
< 0.1%
1.254
 
0.1%
1.454
 
0.1%
1.641
 
< 0.1%
1.655
 
0.1%
1.72
 
< 0.1%
1.791
 
< 0.1%
ValueCountFrequency (%)
22998.41
< 0.1%
14688.241
< 0.1%
8511.151
< 0.1%
7443.591
< 0.1%
5228.41
< 0.1%
4815.261
< 0.1%
4814.741
< 0.1%
4486.241
< 0.1%
44291
< 0.1%
3677.151
< 0.1%

Interactions

2022-05-04T16:23:21.080215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:28.647835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:32.400339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:36.624792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:40.459982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:44.392768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:48.140051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:51.649731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:56.445162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:00.407243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:04.057918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:08.727226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:12.604219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:16.502712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:21.329073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:28.911852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:32.693169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:36.880649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:40.814198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:44.663357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:48.399024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:52.188497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:56.732559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:00.697428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:04.338756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:09.088019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:12.848087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:16.742191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:21.548944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:29.133272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:33.097213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:37.143663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:41.100162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:44.911453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:48.614901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:52.399588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:56.980547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:00.957207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:04.613615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:09.347957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:13.110412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:17.044107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:21.973701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:29.374530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:33.465003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:37.422067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:41.455960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:45.194288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:48.898883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:52.706419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:57.221985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:01.255045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:04.857458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:09.642785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:13.757213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:17.318964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:22.564361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:29.700533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:33.704976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:37.693912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:41.682965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:45.451157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:49.125754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:53.031237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:57.492048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:01.498468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:05.121199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:09.906731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:14.009957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:17.580957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:22.912161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:30.015011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:33.966843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:38.047707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:42.187963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:45.744972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:49.409135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:53.530599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:57.840077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:01.785304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:05.828794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:10.186975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:14.280073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:18.040950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:23.152035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:30.248878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:34.299250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:38.324115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:42.406088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:46.012955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:49.633012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:53.872417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:58.129912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:02.011173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:06.154729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:10.419851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:14.554150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:18.608976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:24.130961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:30.500735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:34.578089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:38.605053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:42.646953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:46.275804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:49.908196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:54.214085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:58.446156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:02.271033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:06.463552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:10.818712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:14.800252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:18.896983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:24.422793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:30.744873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:34.814472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:38.933866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:42.932785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:46.560765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:50.186141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:54.580743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:58.749388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:02.544212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:06.861321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:11.092589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:15.083998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:19.257772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:24.789582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:30.995840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:35.106670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:39.192302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:43.160989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:46.822948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:50.464217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:54.933540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:59.049264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:02.781885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:07.180138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:11.328587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:15.331726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:19.507629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:25.122392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:31.328670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:35.378507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:39.437171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:43.448170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:47.109008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:50.715373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:55.356295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:59.349334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:03.067839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:07.490139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:11.613422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:15.622196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:19.822449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:25.496706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:31.685465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:35.843240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:39.690293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:43.696030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:47.379855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:50.971478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:55.666728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:59.631391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:03.313946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:07.762083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:11.871408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:15.889959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:20.076308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:25.809322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:31.925625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:36.132075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:39.966136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:43.926035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:47.627192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:51.203660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:55.929577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:59.889241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:03.542965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:08.140227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:12.125396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:16.120934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:20.410599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:26.173269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:32.164206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:36.387927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:40.202124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:44.159900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:47.894195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:51.412838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:22:56.199152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:00.139279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:03.782943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:08.472152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:12.373352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:16.307841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-04T16:23:20.786381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-04T16:23:37.671139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-04T16:23:38.170833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-04T16:23:38.603826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-04T16:23:39.039994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-04T16:23:26.690947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-04T16:23:27.460505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

customer_idrevenuerecencyquantity_ordersquantity_items_purchasedavg_ticketavg_recencytime_in_basefrequencyfrequency_btwn_purchasesavg_basket_sizeavg_unique_basked_sizequantity_items_returnedmonetary_returned
0178505391.21372341733158.565000186.5000003740.09090917.00000050.9705880.61764740102.58
1130473232.595691390359.17666753.2857143740.0240640.028302154.44444411.66666735143.49
2125836705.382155028447.02533324.8666673740.0401070.040323335.2000007.6000005076.04
313748948.25955439189.65000093.2500003740.0133690.01792187.8000004.80000000.00
415100876.00333380292.000000124.3333333740.0080210.07317126.6666670.33333322240.90
5152914623.3025142102330.23571426.6428573740.0374330.040115150.1428574.3571432971.79
6146885630.877213621268.13666718.6500003740.0561500.057221172.4285717.047619399523.49
7178095411.9116122057450.99250037.3000003740.0320860.033520171.4166673.8333334167.06
81531160767.9009138194667.7791214.1444443740.2433160.243316419.7142866.2307694741348.56
9160982005.63877613286.51857153.2857143740.0187170.02439087.5714294.85714300.00

Last rows

customer_idrevenuerecencyquantity_ordersquantity_items_purchasedavg_ticketavg_recencytime_in_basefrequencyfrequency_btwn_purchasesavg_basket_sizeavg_unique_basked_sizequantity_items_returnedmonetary_returned
5683226956083.951118526083.951.020.51.01852.0675.000.0
5684226967150.071121507150.071.020.51.02150.0748.000.0
5685226993686.80116913686.801.020.51.0691.0203.000.0
5686227004839.421110744839.421.020.51.01074.055.000.0
56872270417.90111417.901.020.51.014.07.000.0
5688227053.351123.351.020.51.02.02.000.0
5689227065699.001117475699.001.020.51.01747.0634.000.0
5690227076756.060120106756.060.011.01.02010.0730.000.0
5691227083217.20016543217.200.011.01.0654.056.000.0
5692227093950.72017313950.720.011.01.0731.0217.000.0